Masked Face Recognition Using MobileNet V2 with Transfer Learning

Abstract

Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

“…Figures 6 and 7 show the accuracy gained using the various approaches listed in Table 3 on the Faces94 and Grimace datasets. On the Faces94 dataset, the suggested method utilising the ensemble approach achieved 100% accuracy, while Kumar et al [15] achieved 99.09% and Sikarwar et al [27] achieved 98.19%. On the Grimace dataset, the suggested method employing the ensemble approach achieved 100% accuracy, while Kumar et al [15] achieved 99.25% and Sikarwar et al [27] achieved 95.32%.…”

“…On the Faces94 dataset, the suggested method utilising the ensemble approach achieved 100% accuracy, while Kumar et al [15] achieved 99.09% and Sikarwar et al [27] achieved 98.19%. On the Grimace dataset, the suggested method employing the ensemble approach achieved 100% accuracy, while Kumar et al [15] achieved 99.25% and Sikarwar et al [27] achieved 95.32%. This demonstrates that the suggested system using the ensemble approach outperformed the other current approaches listed in Table 3.…”

How Transfer Learning Is Used in Generative Models forImage Classification: Improved Accuracy

“…Ahsan et al [43] evaluated the efficiency of various models, including ResNet101, for feature extraction in diagnosing monkeypox disease. The model's application extends to face mask detection problems [44], where it is utilized alongside deep neural networks. Additionally, ResNet101 has found practical applications in diverse domains, including industry [45], agriculture [46], and meteorology [47], showcasing its adaptability and effectiveness in handling various image processing challenges.…”

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

“…In addition to this, we also evaluated the processing speed of different feature extraction networks. For example, when using MobileNet-V2-0.35 [35], the model can reach a processing speed of 16.5 frames/sed but with a relatively high error rate. The slowest detection speed is EfficientNet-b6, about 3.8 frames/sec.…”